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DV0101EN-Final_Assign_Part_2_Questions (1).py
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DV0101EN-Final_Assign_Part_2_Questions (1).py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
# Load the data using pandas
data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/historical_automobile_sales.csv')
# Initialize the Dash app
app = dash.Dash(__name__)
# Set the title of the dashboard
#app.title = "Automobile Sales Statistics Dashboard"
#---------------------------------------------------------------------------------
# Create the dropdown menu options
dropdown_options = [
{'label': ' Yearly Statistics', 'value': 'Yearly Statistics'},
{'label': 'Recession Period Statistics', 'value': 'Recession Period Statistics'}
]
# List of years
year_list = [i for i in range(1980, 2024, 1)]
#---------------------------------------------------------------------------------------
# Create the layout of the app
app.layout = html.Div([
#TASK 2.1 Add title to the dashboard
html.H1("Automobile Sales Statistics Dashboard",style={'textAlign':'center','color':'#503D36','font-size':24}),#May include style for title
html.Div([#TASK 2.2: Add two dropdown menus
html.Label("Select Statistics:"),
dcc.Dropdown(
id='dropdown-statistics',
options= dropdown_options,
value='Select Statistics',
placeholder='Select a report type'
)
]),
html.Div(dcc.Dropdown(
id='select-year',
options=[{'label': i, 'value': i} for i in year_list],
value='Select Year'
)),
html.Div([#TASK 2.3: Add a division for output display
html.Div(id='output-container', className='chart-grid', style={'display':'flex'})])
])
#TASK 2.4: Creating Callbacks
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='select year', component_property='disabled'),
Input(component_id='dropdown-statistics',component_property='value'))
def update_input_container(selected_statistics):
if selected_statistics =='Yearly Statistics':
return False
else:
return True
#Callback for plotting
# Define the callback function to update the input container based on the selected statistics
@app.callback(
Output(component_id='output-container', component_property='children'),
[Input(component_id='dropdown-statistics', component_property='value'), Input(component_id='select-year', component_property='value')])
def update_output_container(selected_statistics, input_year):
if selected_statistics == 'Recession Period Statistics':
# Filter the data for recession periods
recession_data = data[data['Recession'] == 1]
#TASK 2.5: Create and display graphs for Recession Report Statistics
#Plot 1 Automobile sales fluctuate over Recession Period (year wise)
# use groupby to create relevant data for plotting
yearly_rec=recession_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
R_chart1 = dcc.Graph(
figure=px.line(yearly_rec,
x='Year',
y='Automobile_Sales',
title="Average Automobile Sales fluctuation over Recession Period"))
#Plot 2 Calculate the average number of vehicles sold by vehicle type
# use groupby to create relevant data for plotting
average_sales = recession_data.groupby('Vehicle_Type')['Automobile_Sales'].mean().reset_index()
R_chart2 = dcc.Graph(figure=px.line(average_sales,y='Automobile_Sales',x='Vehicle_Type',title='average number of vehicles sold by vehicle type'))
# Plot 3 Pie chart for total expenditure share by vehicle type during recessions
# use groupby to create relevant data for plotting
exp_rec= recession_data.groupby('Vehicle_Type')['Advertising_Expenditure'].sum().reset_index()
R_chart3 = dcc.Graph(
figure=px.pie(exp_rec,values='Advertising_Expenditure',names='Vehicle_Type',title='total expenditure share by vehicle type during recessions')
)
# Plot 4 bar chart for the effect of unemployment rate on vehicle type and sales
unemploy_rate=recession_data.groupby('Unemployment_Rate','Vehicle_Type')['Automobile_Sales'].sum().reset_index()
R_chart4= dcc.Graph(figure=px.bar(unemploy_rate,x='Unemployment_Rate',y='Automobile_Sales',color='Vehicle_Type',title='effect of unemployment rate on vehicle type and sales'))
return [
html.Div(className='chart-item', children=[html.Div(children=R_chart1),html.Div(children=R_chart2)],style={'display':'flex'}),
html.Div(className='chart-item', children=[html.Div(children=R_chart3),html.Div(children=R_chart4)],style={'display':'flex'})
]
# TASK 2.6: Create and display graphs for Yearly Report Statistics
# Yearly Statistic Report Plots
elif (input_year and selected_statistics=='Yearly Statistics') :
yearly_data = data[data['Year'] == input_year]
#TASK 2.5: Creating Graphs Yearly data
#plot 1 Yearly Automobile sales using line chart for the whole period.
yas= data.groupby('Year')['Automobile_Sales'].mean().reset_index()
Y_chart1 = dcc.Graph(figure=px.line(yas,x='Year',y='Automobile_Sales',title='Yearly Automobile sales'))
# Plot 2 Total Monthly Automobile sales using line chart.
mas= yearly_data.groupby('Month')['Automobile_Sales'].sum().reset_index()
Y_chart2 = dcc.Graph(figure=px.line(mas,x='Month',y='Automobile_Sales',title='Total Monthly Automobile Sales'))
# Plot bar chart for average number of vehicles sold during the given year
avr_vdata=yearly_data.groupby('Year')['Automobile_Sales'].mean().reset_index()
Y_chart3 = dcc.Graph( figure=px.bar(avr_vdata,x='Year',y='Automobile Sales' ,title='Average Vehicles Sold by Vehicle Type in the year {}'.format(input_year)))
# Total Advertisement Expenditure for each vehicle using pie chart
exp_data=yearly_data.groupby('Vehicle_Type')['Advertisement_Expenditure'].sum().reset_index()
Y_chart4 = dcc.Graph(figure=px.pie(exp_data,values='Advertisement_Expenditure',names='Vehicle_Type'))
#TASK 2.6: Returning the graphs for displaying Yearly data
return [
html.Div(className='chart-item', children=[html.Div(Y_chart1),html.Div(Y_chart2)],style={'display': 'flex'}),
html.Div(className='chart-item', children=[html.Div(Y_chart3),html.Div(Y_chart4)],style={'display': 'flex'})
]
else:
return None
# Run the Dash app
if __name__ == '__main__':
app.run_server()